BoostUp Financial Model

BoostUp positions itself as an "Analytical System of Record" sitting between transactional CRMs (Salesforce, Oracle, MS Dynamics, SAP) and end-user engagement tools (Gmail, Outlook, Zoom, Slack, LinkedIn, Outreach, Marketo, Zendesk, Drift, Jira).

Core claims:

• Automatically extracts all digital engagement data (email, calendar, calls, video, IM) - no manual CRM input required
• Provides real-time, unbiased deal intelligence vs. "biased, late, half-truth" CRM data
• Two primary product modules visible in UI: (1) Opportunity/Deal Intelligence panel with AI risk scores, sentiment alerts, relationship mapping; (2) Forecasting module with waterfall pipeline trend charts, commit/best-case/pipeline breakdowns by seller
• Consolidates fragmented revenue intelligence market: Forecast Intelligence, Pipeline Intelligence, Account Intelligence, Activity Intelligence - all in one platform
• Use cases: Forecast Management, Deal & Account Management, QBRs, Ramp & Readiness, Annual Planning, Performance Assessments

TAM defined as 38,318 global B2B companies with revenue team size ≥50, totalling $6.6B:

• SaaS subscription, sold to B2B enterprises
• ACV tiers imply per-seat or team-based pricing scaled by revenue team size: $75K ACV for 50–100-seat teams up to $1M ACV for 1,000+ seat teams
• Pricing is likely per-seat/per-user annually, land-and-expand motion - consistent with sales-tech comps and the CRO-buyer personas described (slide 12). No explicit per-seat price quoted in deck.
• GTM: Direct enterprise sales (CCO co-founder; sales-led motion implied)
• No mention of freemium, marketplace, or usage-based tiers

• "3X Growth Since Jan 1st, 2020" - metric undefined (revenue, ARR, customers, or seats - not specified)
• Named customers (9 logos shown): Branch, Iterable, Toluna, PerimeterX, Cognite, Rancher, NetApp, Grand Rounds, Dealpath
• No ARR, MRR, customer count, NRR, or churn figures disclosed

Competitive framing: BoostUp "consolidates today's fragmented revenue intelligence market", implying it replaces point solutions. Named integrations suggest competitive displacement of standalone tools in each category.

Moat claims (qualitative):

• Contextual data layer: combines unstructured data from all digital channels vs. CRM-only rivals
• Platform play ("Data Platform Play vs. Single Use Case Play") vs. single-use-case apps
• AI forecasting powered by contextual signals, not just CRM inputs

Named competitors: Not explicitly named in deck. Implicit displacement targets: Salesforce Einstein, Clari, Gong, Chorus, People.ai (not named but implied by market consolidation framing).

Team:

• Sharad Verma - CEO & Co-Founder: Search AI at Yahoo, Founder/CEO Piqora, VP Products Bloomreach
• Amit Sasturkar - CTO & Co-Founder: Search Engineer at Google, Founder/CTO OpsClarity, Search Engineer Yahoo
• Neel Kamal - CCO & Co-Founder: VP Sales Aviatrix, Sales VMware, Sales Engineer IBM

Archetype + why: SaaS ARR model with ACV cohort build-up and seat-based expansion. Revenue is subscription-based with a clear tiered ACV structure by customer size. The deck's TAM table provides natural ACV buckets to model as separate cohorts. Land-and-expand dynamics (CRO platform with multiple modules) make net revenue retention a key output.

Forecast horizon & granularity:

• 5-year annual model (Year 1–5), with Year 1 broken into monthly for cash-flow / runway visibility
• Quarterly granularity for ARR bridge (New ARR, Expansion ARR, Churned ARR, Net New ARR)

Key drivers & assumptions:

*Revenue:*

• Starting customer count: ~9 named logos visible; assume ~10–15 paying customers at time of raise; rationale: 9 logos shown, likely more unnamed
• New logos per year by segment (SMB 50–100 / Mid 101–500 / Enterprise 500+): enterprise-led motion, start heavy mid-market
• ACV by segment: $75K / $200–300K / $500K–$1M
• ACV growth rate (expansion): 10–20% YoY per cohort; typical for seat-expansion in sales tech
• Net Revenue Retention: 110–120%; consistent with best-in-class sales-tech peers selling to growing revenue teams
• Logo churn rate: 8–12% annually; early-stage enterprise SaaS benchmark
• 3x growth since Jan 2020 - apply to seed ARR as implied CAGR starting point

*Costs:*

• Gross margin: 70–75%; SaaS infrastructure + data ingestion costs; slightly below pure SaaS due to data processing layer
• S&M % of revenue: 50–60% in early years; enterprise direct-sales motion with high CAC
• R&D % of revenue: 30–35%; AI/ML platform requires heavy engineering
• G&A % of revenue: 10–15%

*Headcount:*

• Seed-stage team of ~15–25 at raise; scale with sales capacity model (AE quota capacity drives new ARR)
• AE quota: $400K–$600K ARR per AE (consistent with mid-market sales-tech)
• Sales cycle: 3–6 months for enterprise deals

Scenarios (Base / Bull / Bear - which variables flex):

• Base: 3x ARR growth in Year 1 continuing traction; moderate NRR 110%; mid-market focus
• Bull: Faster enterprise up-sell (higher ACV mix), NRR 120%+, platform consolidation wins (replacing 3+ point tools per customer)
• Bear: Long sales cycles, logo churn above 15%, ACV pressure as market becomes crowded (Clari, Gong competition)

Required sheets / outputs:

1. Assumptions (all inputs, colour-coded)
2. ARR Waterfall (New / Expansion / Churn / Net New ARR by year)
3. Customer Cohort Table (by ACV tier: SMB / Mid / Enterprise)
4. P&L (Revenue → Gross Profit → EBITDA)
5. Headcount & Opex Build
6. Cash & Runway (monthly Year 1, annual Year 2–5)
7. KPI Dashboard (ARR, NRR, CAC, LTV, LTV:CAC, Magic Number, Rule of 40)
8. Sensitivity (NRR × logo growth rate → ARR Year 3)